!pip install pmdarima
Collecting pmdarima
Downloading pmdarima-2.0.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (1.9 MB)
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Collecting numpy>=1.21.2
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Installing collected packages: numpy, pmdarima
Attempting uninstall: numpy
Found existing installation: numpy 1.19.5
Uninstalling numpy-1.19.5:
Successfully uninstalled numpy-1.19.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
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!pip install --upgrade numpy
Requirement already satisfied: numpy in /opt/conda/miniconda3/lib/python3.8/site-packages (1.22.4)
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Using cached numpy-1.23.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)
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Attempting uninstall: numpy
Found existing installation: numpy 1.22.4
Uninstalling numpy-1.22.4:
Successfully uninstalled numpy-1.22.4
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pointpats 2.2.0 requires opencv-contrib-python>=4.2.0, which is not installed.
scipy 1.6.3 requires numpy<1.23.0,>=1.16.5, but you have numpy 1.23.5 which is incompatible.
pysal 2.4.0 requires urllib3>=1.26, but you have urllib3 1.25.11 which is incompatible.
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Note: you may need to restart the kernel to use updated packages.
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
from google.cloud import storage
from pyspark.sql.functions import lit
from functools import reduce
from pyspark.sql import DataFrame
from pyspark.sql.functions import *
from datetime import datetime
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.arima.model import ARIMA
from pmdarima.arima import auto_arima
from pyspark.sql.window import Window
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
spark = SparkSession.builder.appName('Nifty50').getOrCreate()
#change configuration settings on Spark
/gateway/default/node/conf?host&port = spark.sparkContext._conf.setAll([('spark.executor.memory', '30g'), ('spark.app.name', 'Spark Updated Conf'), ('spark.executor.cores', '4'), ('spark.cores.max', '4'), ('spark.driver.memory','30g'), ("spark.driver.maxResultSize", "70g")])
gcs_client = storage.Client()
bucket_name = 'new_bigdata_nifty50'
bucket = gcs_client.bucket(bucket_name)
file_name = 'df_with_indicators.csv'
df = spark.read.csv('gs://{}//{}'.format(bucket_name, file_name), inferSchema=True)
new_columns = ['date',
'close',
'high',
'low',
'open',
'volume',
'sma5',
'sma10',
'sma15',
'sma20',
'ema5',
'ema10',
'ema15',
'ema20',
'upperband',
'middleband',
'lowerband',
'HT_TRENDLINE',
'KAMA10',
'KAMA20',
'KAMA30',
'SAR',
'TRIMA5',
'TRIMA10',
'TRIMA20',
'ADX5',
'ADX10',
'ADX20',
'APO',
'CCI5',
'CCI10',
'CCI15',
'macd510',
'macd520',
'macd1020',
'macd1520',
'macd1226',
'MFI',
'MOM10',
'MOM15',
'MOM20',
'ROC5',
'ROC10',
'ROC20',
'PPO',
'RSI14',
'RSI8',
'slowk',
'slowd',
'fastk',
'fastd',
'fastksr',
'fastdsr',
'ULTOSC',
'WILLR',
'ATR',
'Trange',
'TYPPRICE',
'HT_DCPERIOD',
'BETA',
'sector',
'company',
'Inflation Rate ',
'Balance of Trade',
'Bank Lending Rate',
'Car Registrations',
'Consumer Price Index',
'Crude Oil Production',
'Fiscal Expenditure',
'Industrial Production',
'Food Inflation',
'Producer Prices',
'Reverse Repo Rate',
'Steel Production',
'Tourist Arrivals',
'Corporate Tax Rate ',
'Export Prices',
'GDP per Capita PPP',
'GDP',
'Gross National Product',
'Import Prices',
'Military Expenditure']
from functools import reduce
old_columns = df.schema.names
df = reduce(lambda data, idx: data.withColumnRenamed(old_columns[idx], new_columns[idx]), range(len(old_columns)), df)
cols = df.columns
cols.remove('company')
cols.remove('sector')
exprs = {x: "avg" for x in cols}
exprs['sector'] = 'first'
date_df = df.groupby(df.company, to_date(df.date).alias('day')).agg(exprs)
cols = df.columns
cols.remove('company')
cols.remove('sector')
exprs = {x: "avg" for x in cols}
exprs['sector'] = 'first'
date_df = df.groupby(df.company, to_date(df.date).alias('day')).agg(exprs)
w = Window.partitionBy('company').orderBy("day")
date_df = date_df.withColumn('diffClose', col("avg(close)") - lag(col("avg(close)")).over(w))
sum_df = date_df.groupby(df.company).agg(sum('diffClose').alias('totalChange'), first('first(sector)').alias('sector'))
w = Window.partitionBy('sector')
sector_df = sum_df.withColumn('maxtotalChange', max('totalChange').over(w))\
.where(col('totalChange') == col('maxtotalChange'))\
.drop('maxtotalChange')
sector_df = sector_df.toPandas()
top_companies_per_sector = list(sector_df['company'])
top_companies_per_sector.remove('NIFTY 50')
top_companies_per_sector.remove('NIFTY BANK')
top_companies_per_sector
['INDIGO', 'ADANIENT', 'MARUTI', 'BHARTIARTL', 'BAJFINANCE', 'APOLLOHOSP', 'ADANIGREEN', 'LTI', 'SHREECEM', 'NESTLEIND', 'SIEMENS', 'LT', 'RELIANCE', 'DMART', 'DLF', 'PIIND', 'ASIANPAINT']
top_companies_df = date_df.filter(date_df.company.isin(top_companies_per_sector)).toPandas()
import pandas as pd
def test_stationarity(timeseries, company):
#Determing rolling statistics
rolmean = timeseries.rolling(12).mean()
rolstd = timeseries.rolling(12).std()
#Plot rolling statistics:
plt.plot(timeseries, color='blue',label='Original')
plt.plot(rolmean, color='red', label='Rolling Mean')
plt.plot(rolstd, color='black', label = 'Rolling Std')
plt.legend(loc='best')
plt.title('Rolling Mean and Standard Deviation for {}'.format(company))
plt.show(block=False)
print("Results of dickey fuller test")
adft = adfuller(timeseries,autolag='AIC')
output = pd.Series(adft[0:4],index=['Test Statistics','p-value','No. of lags used','Number of observations used'])
for key,values in adft[4].items():
output['critical value (%s)'%key] = values
print(output)
for company in top_companies_per_sector:
test_stationarity(top_companies_df[top_companies_df['company']==company]['avg(close)'], company)
Results of dickey fuller test Test Statistics -1.737789 p-value 0.411755 No. of lags used 9.000000 Number of observations used 1580.000000 critical value (1%) -3.434496 critical value (5%) -2.863371 critical value (10%) -2.567745 dtype: float64
Results of dickey fuller test Test Statistics 2.935496 p-value 1.000000 No. of lags used 21.000000 Number of observations used 1747.000000 critical value (1%) -3.434099 critical value (5%) -2.863196 critical value (10%) -2.567652 dtype: float64
Results of dickey fuller test Test Statistics -1.781028 p-value 0.389921 No. of lags used 9.000000 Number of observations used 1787.000000 critical value (1%) -3.434015 critical value (5%) -2.863159 critical value (10%) -2.567632 dtype: float64
Results of dickey fuller test Test Statistics -0.201060 p-value 0.938395 No. of lags used 1.000000 Number of observations used 1795.000000 critical value (1%) -3.433998 critical value (5%) -2.863152 critical value (10%) -2.567628 dtype: float64
Results of dickey fuller test Test Statistics -0.497503 p-value 0.892503 No. of lags used 10.000000 Number of observations used 1786.000000 critical value (1%) -3.434017 critical value (5%) -2.863160 critical value (10%) -2.567632 dtype: float64
Results of dickey fuller test Test Statistics 0.338065 p-value 0.979025 No. of lags used 18.000000 Number of observations used 1778.000000 critical value (1%) -3.434033 critical value (5%) -2.863167 critical value (10%) -2.567636 dtype: float64
Results of dickey fuller test Test Statistics -0.277376 p-value 0.928648 No. of lags used 21.000000 Number of observations used 974.000000 critical value (1%) -3.437082 critical value (5%) -2.864512 critical value (10%) -2.568352 dtype: float64
Results of dickey fuller test Test Statistics -0.842076 p-value 0.806449 No. of lags used 23.000000 Number of observations used 1433.000000 critical value (1%) -3.434922 critical value (5%) -2.863559 critical value (10%) -2.567845 dtype: float64
Results of dickey fuller test Test Statistics -2.051256 p-value 0.264561 No. of lags used 2.000000 Number of observations used 1794.000000 critical value (1%) -3.434000 critical value (5%) -2.863152 critical value (10%) -2.567628 dtype: float64
Results of dickey fuller test Test Statistics 0.089650 p-value 0.965347 No. of lags used 9.000000 Number of observations used 1787.000000 critical value (1%) -3.434015 critical value (5%) -2.863159 critical value (10%) -2.567632 dtype: float64
Results of dickey fuller test Test Statistics 0.134510 p-value 0.968340 No. of lags used 1.000000 Number of observations used 1795.000000 critical value (1%) -3.433998 critical value (5%) -2.863152 critical value (10%) -2.567628 dtype: float64
Results of dickey fuller test Test Statistics -0.865986 p-value 0.798967 No. of lags used 2.000000 Number of observations used 1766.000000 critical value (1%) -3.434058 critical value (5%) -2.863178 critical value (10%) -2.567642 dtype: float64
Results of dickey fuller test Test Statistics -0.367526 p-value 0.915381 No. of lags used 23.000000 Number of observations used 1773.000000 critical value (1%) -3.434044 critical value (5%) -2.863172 critical value (10%) -2.567639 dtype: float64
Results of dickey fuller test Test Statistics -0.511339 p-value 0.889785 No. of lags used 7.000000 Number of observations used 1295.000000 critical value (1%) -3.435410 critical value (5%) -2.863774 critical value (10%) -2.567960 dtype: float64
Results of dickey fuller test Test Statistics -1.041000 p-value 0.738018 No. of lags used 8.000000 Number of observations used 1788.000000 critical value (1%) -3.434013 critical value (5%) -2.863158 critical value (10%) -2.567631 dtype: float64
Results of dickey fuller test Test Statistics 0.031716 p-value 0.961078 No. of lags used 5.000000 Number of observations used 1791.000000 critical value (1%) -3.434006 critical value (5%) -2.863155 critical value (10%) -2.567630 dtype: float64
Results of dickey fuller test Test Statistics -0.111569 p-value 0.948274 No. of lags used 20.000000 Number of observations used 1776.000000 critical value (1%) -3.434037 critical value (5%) -2.863169 critical value (10%) -2.567637 dtype: float64
import numpy as np
for company in top_companies_per_sector:
company_log = np.log(top_companies_df[top_companies_df['company']==company]['avg(close)'])
company_log.index = pd.to_datetime(company_log.index)
result = seasonal_decompose(company_log, model='multiplicative', period=1)
fig = plt.figure(figsize=(16, 9))
fig = result.plot()
fig.set_size_inches(16, 9)
fig.suptitle('Sesonal Decompose of Close Price of {}'.format(company))
/tmp/ipykernel_8625/702561032.py:6: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = plt.figure(figsize=(16, 9))
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from statsmodels.tsa.arima.model import ARIMA
top_companies_df.columns
Index(['company', 'day', 'avg(Reverse Repo Rate)', 'avg(RSI14)',
'avg(Car Registrations)', 'avg(CCI10)', 'avg(TRIMA10)', 'avg(SAR)',
'avg(fastdsr)', 'avg(ADX20)', 'avg(WILLR)', 'avg(sma10)',
'avg(macd1226)', 'avg(sma20)', 'avg(ema15)', 'avg(ema10)',
'avg(KAMA10)', 'avg(TRIMA20)', 'avg(Tourist Arrivals)', 'avg(fastksr)',
'avg(Steel Production)', 'avg(Gross National Product)', 'avg(TRIMA5)',
'avg(macd1020)', 'avg(CCI5)', 'avg(MOM15)', 'avg(Export Prices)',
'avg(TYPPRICE)', 'avg(GDP per Capita PPP)', 'avg(HT_DCPERIOD)',
'avg(ema5)', 'first(sector)', 'avg(open)', 'avg(Producer Prices)',
'avg(PPO)', 'avg(ADX10)', 'avg(CCI15)', 'avg(Fiscal Expenditure)',
'avg(Import Prices)', 'avg(ATR)', 'avg(Crude Oil Production)',
'avg(upperband)', 'avg(low)', 'avg(BETA)', 'avg(ROC20)', 'avg(macd520)',
'avg(date)', 'avg(MOM10)', 'avg(fastk)', 'avg(MFI)',
'avg(Corporate Tax Rate )', 'avg(APO)', 'avg(sma5)', 'avg(KAMA20)',
'avg(RSI8)', 'avg(macd510)', 'avg(lowerband)',
'avg(Military Expenditure)', 'avg(sma15)', 'avg(fastd)', 'avg(ADX5)',
'avg(slowd)', 'avg(ema20)', 'avg(Consumer Price Index)',
'avg(Industrial Production)', 'avg(KAMA30)', 'avg(GDP)',
'avg(Bank Lending Rate)', 'avg(Trange)', 'avg(MOM20)',
'avg(Balance of Trade)', 'avg(HT_TRENDLINE)', 'avg(ULTOSC)',
'avg(Inflation Rate )', 'avg(ROC5)', 'avg(close)',
'avg(Food Inflation)', 'avg(middleband)', 'avg(ROC10)', 'avg(volume)',
'avg(macd1520)', 'avg(high)', 'avg(slowk)', 'diffClose'],
dtype='object')
for company in top_companies_per_sector:
df_log = np.log(top_companies_df[top_companies_df['company'] == company].set_index('day')['avg(sma10)'])
train_data, test_data = df_log[3:int(len(df_log)*0.9)], df_log[int(len(df_log)*0.9):]
model_autoARIMA = auto_arima(train_data, start_p=0, start_q=0,
test='adf', # use adftest to find optimal 'd'
max_p=3, max_q=3, # maximum p and q
m=1, # frequency of series
d=None, # let model determine 'd'
seasonal=False, # No Seasonality
start_P=0,
D=0,
trace=False,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
print('ARIMA Summary for {}\n: {}'.format(company,model_autoARIMA.summary()))
model_autoARIMA.plot_diagnostics(figsize=(15,8))
plt.title('ARIMA Plot for {}'.format(company))
fc, /gateway/default/node/conf?host&port = model_autoARIMA.predict(n_periods=len(test_data), index=test_data.index, return_conf_int=True)
fc_series = pd.DataFrame(list(zip(fc,test_data.index)), columns=['fitted', 'date']).set_index('date')
lower_series = pd.Series(/gateway/default/node/conf?host&port[:, 0], index=test_data.index)
upper_series = pd.Series(/gateway/default/node/conf?host&port[:, 1], index=test_data.index)
#fc = fitted.forecast(len(test_data), alpha=0.05)
#fc_series = pd.DataFrame(list(zip(fc,test_data.index)), columns=['fitted', 'date']).set_index('date')
# Plot
plt.figure(figsize=(10,5), dpi=100)
plt.plot(train_data, label='training data')
plt.plot(test_data, color = 'blue', label='Actual Stock Price')
plt.plot(fc_series, color = 'orange',label='Predicted Stock Price')
plt.fill_between(lower_series.index, lower_series, upper_series,
color='k', alpha=.10)
plt.title('{} Stock Price Prediction'.format(company))
plt.xlabel('Time')
plt.ylabel('{} Stock Price'.format(company))
plt.legend(loc='upper left', fontsize=8)
plt.show()
ARIMA Summary for INDIGO
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1428
Model: SARIMAX(1, 1, 0) Log Likelihood 3309.273
Date: Wed, 30 Nov 2022 AIC -6614.545
Time: 04:01:58 BIC -6604.018
Sample: 0 HQIC -6610.614
- 1428
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.1973 0.022 9.142 0.000 0.155 0.240
sigma2 0.0006 8.11e-06 69.801 0.000 0.001 0.001
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 8507.48
Prob(Q): 0.91 Prob(JB): 0.00
Heteroskedasticity (H): 1.14 Skew: -1.13
Prob(H) (two-sided): 0.14 Kurtosis: 14.74
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for ADANIENT
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1589
Model: SARIMAX(3, 1, 1) Log Likelihood 2566.643
Date: Wed, 30 Nov 2022 AIC -5123.286
Time: 04:02:11 BIC -5096.435
Sample: 0 HQIC -5113.312
- 1589
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.4702 0.274 1.713 0.087 -0.068 1.008
ar.L2 -0.0681 0.058 -1.169 0.242 -0.182 0.046
ar.L3 0.1014 0.035 2.864 0.004 0.032 0.171
ma.L1 -0.3077 0.276 -1.113 0.266 -0.849 0.234
sigma2 0.0023 9.76e-06 236.617 0.000 0.002 0.002
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 22851976.22
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 0.17 Skew: -18.79
Prob(H) (two-sided): 0.00 Kurtosis: 589.48
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for MARUTI
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(1, 1, 0) Log Likelihood 4379.565
Date: Wed, 30 Nov 2022 AIC -8755.130
Time: 04:02:15 BIC -8744.359
Sample: 0 HQIC -8751.132
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.1824 0.016 11.348 0.000 0.151 0.214
sigma2 0.0003 4.03e-06 63.655 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 4443.68
Prob(Q): 0.98 Prob(JB): 0.00
Heteroskedasticity (H): 2.08 Skew: -0.35
Prob(H) (two-sided): 0.00 Kurtosis: 11.10
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for BHARTIARTL
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 4191.650
Date: Wed, 30 Nov 2022 AIC -8379.301
Time: 04:02:20 BIC -8368.529
Sample: 0 HQIC -8375.303
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ma.L1 0.1206 0.021 5.632 0.000 0.079 0.163
sigma2 0.0003 7.88e-06 41.053 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 328.28
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 1.37 Skew: 0.24
Prob(H) (two-sided): 0.00 Kurtosis: 5.16
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for BAJFINANCE
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 3830.036
Date: Wed, 30 Nov 2022 AIC -7654.072
Time: 04:02:24 BIC -7637.914
Sample: 0 HQIC -7648.075
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0018 0.001 2.786 0.005 0.001 0.003
ma.L1 0.1538 0.015 9.931 0.000 0.123 0.184
sigma2 0.0005 8.09e-06 62.617 0.000 0.000 0.001
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 4315.75
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 1.70 Skew: -0.49
Prob(H) (two-sided): 0.00 Kurtosis: 10.95
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for APOLLOHOSP
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 4055.343
Date: Wed, 30 Nov 2022 AIC -8106.685
Time: 04:02:26 BIC -8095.914
Sample: 0 HQIC -8102.687
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ma.L1 0.1810 0.016 11.542 0.000 0.150 0.212
sigma2 0.0004 7.02e-06 54.579 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.05 Jarque-Bera (JB): 1969.53
Prob(Q): 0.82 Prob(JB): 0.00
Heteroskedasticity (H): 1.84 Skew: 0.45
Prob(H) (two-sided): 0.00 Kurtosis: 8.34
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for ADANIGREEN
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 893
Model: SARIMAX(0, 1, 1) Log Likelihood 1624.968
Date: Wed, 30 Nov 2022 AIC -3243.936
Time: 04:02:30 BIC -3229.556
Sample: 0 HQIC -3238.440
- 893
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0049 0.002 2.675 0.007 0.001 0.008
ma.L1 0.2066 0.027 7.621 0.000 0.153 0.260
sigma2 0.0015 1.76e-05 87.138 0.000 0.001 0.002
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 90344.83
Prob(Q): 0.95 Prob(JB): 0.00
Heteroskedasticity (H): 0.30 Skew: 3.54
Prob(H) (two-sided): 0.00 Kurtosis: 51.79
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for LTI
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1308
Model: SARIMAX(3, 1, 0) Log Likelihood 3249.926
Date: Wed, 30 Nov 2022 AIC -6489.852
Time: 04:02:34 BIC -6463.975
Sample: 0 HQIC -6480.145
- 1308
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0015 0.001 2.706 0.007 0.000 0.003
ar.L1 0.0916 0.017 5.520 0.000 0.059 0.124
ar.L2 0.0513 0.019 2.760 0.006 0.015 0.088
ar.L3 -0.0482 0.025 -1.965 0.049 -0.096 -0.000
sigma2 0.0004 8.68e-06 46.702 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1437.58
Prob(Q): 0.95 Prob(JB): 0.00
Heteroskedasticity (H): 1.86 Skew: 0.34
Prob(H) (two-sided): 0.00 Kurtosis: 8.09
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for SHREECEM
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(1, 0, 2) Log Likelihood 4252.718
Date: Wed, 30 Nov 2022 AIC -8495.437
Time: 04:02:55 BIC -8468.505
Sample: 0 HQIC -8485.440
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0230 0.017 1.327 0.184 -0.011 0.057
ar.L1 0.9976 0.002 557.474 0.000 0.994 1.001
ma.L1 0.1545 0.021 7.289 0.000 0.113 0.196
ma.L2 0.0549 0.024 2.250 0.024 0.007 0.103
sigma2 0.0003 7.1e-06 42.257 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 430.92
Prob(Q): 0.97 Prob(JB): 0.00
Heteroskedasticity (H): 0.81 Skew: 0.31
Prob(H) (two-sided): 0.01 Kurtosis: 5.46
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for NESTLEIND
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 4668.159
Date: Wed, 30 Nov 2022 AIC -9330.318
Time: 04:02:59 BIC -9314.161
Sample: 0 HQIC -9324.321
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0006 0.000 1.673 0.094 -0.000 0.001
ma.L1 0.1190 0.014 8.497 0.000 0.092 0.146
sigma2 0.0002 3.44e-06 52.136 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1693.49
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.85 Skew: 0.21
Prob(H) (two-sided): 0.06 Kurtosis: 8.00
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for SIEMENS
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(1, 1, 0) Log Likelihood 4300.283
Date: Wed, 30 Nov 2022 AIC -8596.566
Time: 04:03:03 BIC -8585.794
Sample: 0 HQIC -8592.568
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.1026 0.020 5.089 0.000 0.063 0.142
sigma2 0.0003 5.63e-06 50.286 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1263.22
Prob(Q): 0.96 Prob(JB): 0.00
Heteroskedasticity (H): 1.12 Skew: 0.04
Prob(H) (two-sided): 0.17 Kurtosis: 7.33
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for LT
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1589
Model: SARIMAX(1, 1, 0) Log Likelihood 4295.035
Date: Wed, 30 Nov 2022 AIC -8586.069
Time: 04:03:07 BIC -8575.329
Sample: 0 HQIC -8582.079
- 1589
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.1707 0.018 9.459 0.000 0.135 0.206
sigma2 0.0003 3.75e-06 69.818 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 7753.59
Prob(Q): 0.92 Prob(JB): 0.00
Heteroskedasticity (H): 1.62 Skew: 0.61
Prob(H) (two-sided): 0.00 Kurtosis: 13.76
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for RELIANCE
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(2, 0, 1) Log Likelihood 4358.280
Date: Wed, 30 Nov 2022 AIC -8706.559
Time: 04:03:28 BIC -8679.627
Sample: 0 HQIC -8696.563
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0021 0.005 0.459 0.646 -0.007 0.011
ar.L1 1.2902 0.085 15.102 0.000 1.123 1.458
ar.L2 -0.2905 0.085 -3.402 0.001 -0.458 -0.123
ma.L1 -0.1212 0.084 -1.444 0.149 -0.286 0.043
sigma2 0.0003 4.13e-06 63.590 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.02 Jarque-Bera (JB): 4443.52
Prob(Q): 0.90 Prob(JB): 0.00
Heteroskedasticity (H): 2.24 Skew: 0.41
Prob(H) (two-sided): 0.00 Kurtosis: 11.09
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for DMART
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1169
Model: SARIMAX(0, 1, 1) Log Likelihood 2938.184
Date: Wed, 30 Nov 2022 AIC -5870.369
Time: 04:03:31 BIC -5855.180
Sample: 0 HQIC -5864.640
- 1169
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0016 0.001 2.288 0.022 0.000 0.003
ma.L1 0.2157 0.021 10.285 0.000 0.175 0.257
sigma2 0.0004 9.38e-06 40.735 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 668.46
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.72 Skew: 0.04
Prob(H) (two-sided): 0.00 Kurtosis: 6.71
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for DLF
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 3576.942
Date: Wed, 30 Nov 2022 AIC -7149.883
Time: 04:03:34 BIC -7139.112
Sample: 0 HQIC -7145.885
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ma.L1 0.1161 0.018 6.554 0.000 0.081 0.151
sigma2 0.0007 1.24e-05 56.064 0.000 0.001 0.001
===================================================================================
Ljung-Box (L1) (Q): 0.01 Jarque-Bera (JB): 2286.06
Prob(Q): 0.94 Prob(JB): 0.00
Heteroskedasticity (H): 0.87 Skew: -0.21
Prob(H) (two-sided): 0.10 Kurtosis: 8.82
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for PIIND
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 4094.138
Date: Wed, 30 Nov 2022 AIC -8182.275
Time: 04:03:39 BIC -8166.118
Sample: 0 HQIC -8176.278
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0011 0.001 2.060 0.039 5.2e-05 0.002
ma.L1 0.0832 0.019 4.271 0.000 0.045 0.121
sigma2 0.0004 6.98e-06 52.341 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1642.41
Prob(Q): 0.99 Prob(JB): 0.00
Heteroskedasticity (H): 1.20 Skew: 0.25
Prob(H) (two-sided): 0.04 Kurtosis: 7.92
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
ARIMA Summary for ASIANPAINT
: SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1614
Model: SARIMAX(0, 1, 1) Log Likelihood 4513.292
Date: Wed, 30 Nov 2022 AIC -9020.585
Time: 04:03:42 BIC -9004.427
Sample: 0 HQIC -9014.587
- 1614
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 0.0009 0.000 2.137 0.033 7.2e-05 0.002
ma.L1 0.0988 0.014 6.847 0.000 0.071 0.127
sigma2 0.0002 4.25e-06 51.095 0.000 0.000 0.000
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 1362.01
Prob(Q): 1.00 Prob(JB): 0.00
Heteroskedasticity (H): 1.35 Skew: 0.11
Prob(H) (two-sided): 0.00 Kurtosis: 7.50
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
/opt/conda/miniconda3/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.